When considering military operations that require rapid response time, forward supply operation of various type of ammunition is essential. Also, t is necessary to supply ammunition in a timely manner before an ammunition shortage situation occurs. In this study, we propose a mathematical model for allocation of ammunition to ammunition storehouse at the Ammunition Supply Post (ASP). The model has several objectives. First, it ensures that the frequent used ammunition is stored in a distributed manner at a high workability ammunition storehouses. Second, infrequent used ammunition is required to be stored intensively at a single storehouse as much as possible. Third, capacity of the storehouse and compatible storage restriction required to be obeyed. Lastly, criticality of ammunition should be considered to ensure safety distance. We propose an algorithm to find the pareto-based optimal solution using the mathematical model in a reasonable computation time. The computational results show that the suggested model and algorithm can solve the real operational scale of the allocation problem.
RAM represents reliability, availability, and maintenance, and is a key performance indicator that is utilized throughout the life cycle of the weapon system from the stage of requesting to disposal. If the RAM target value is set to a too high value, development may be delayed or development cost may be increased. If it is set to a too low value, frequent failures may occur during training, or a problem may arise that the number of available weapon systems is insufficient. The currently most used method to set the RAM target value is to write an operation mode summary and calculate it through an equation. However, as the definition of the operation mode is not standardized, different RAM target values may be set depending on the authoring organization. This study aims to analyze the current situation and suggest an alternative to this problem.
In defense acquisition system, testing and evaluation is a very important procedure that can ensure the completeness of capability while deciding whether to mass-produce or purchase weapons systems. But it always includes realistic restrictions that involve a variety of stakeholders, but lack of time, resources, and budget. Therefore, in the process of planning a test and evaluation, proper number of prototypes and reliability of test results, along with test items and evaluation criteria, are frequently discussed as sensitive agendas. In reality, however, rather than statistical judgments, the number of prototypes and tests are determined by business logic such as duration and budget. Otherwise, most theoretical studies do not adequately reflect the business logic of test assessment. In this study, we propose a number of prototype and tests method that can statistically reasonably verify the performance of the inorganic system considering the characteristics of each test and evaluation project. To this end, we consider the theory related to determining the number of prototypes and tests, and present examples by separating whether to secure the magnitude of effects that have a significant impact on statistical judgment. This study could contribute to the development of empirical methodologies that can adequately coordinate reality and theory in the field of defense test evaluation while ensuring statistical reliability of test evaluation results.
In this paper, following the recent development of optical transmission device equipped with Post-Quantum Cryptography, it proposes to improve the level of defense security system by applying USB security token with Q-PUF USB technology. Although trends of quantum computer development are currently in progress, quantum computer, which have been developed so far, have exceeded the computational capabilities of existing supercomputers. This allows us to easily decode the public certificates and encryption keys. Although the frequency of using public certificates is decreasing due to the abolition of public certificates in May 2020, Republic of Korea Army still uses public certificates for various defense authentication systems. This reality calls for a stronger security systems. Meanwhile, korean technology companies have developed a portable USB security token that can increase security against the use of the quantum computer, and the author suggests the application of it at the defense environment. This report suggests the application of the defense and its expected effectiveness.
This study pursues to solve a batch of nonlinear parameter estimation (NPE) problems where a model interpreting the independent and the dependent variables is given and fixed but corresponding data sets vary. Specifically, we assume that the model does not have an explicit form and the discrepancy between a value from a data set and a corresponding value from the model is unknown. Due to the complexity of the problem, one may prefer to use heuristic algorithms rather than gradient-based algorithms, but the performance of the heuristic algorithms depends on their initial settings. In this study, we suggest two schemes to improve the performance of heuristic algorithms to solve the target problem. Most of all, we apply a Bayesian optimization to find the best parameters of the heuristic algorithm for solving the first NPE problem of the batch and apply the parameters of the heuristic algorithm for solving other NPE problems. Besides, we save a list of simulation outputs obtained from the Bayesian optimization and then use the list to construct the initial population set of the heuristic algorithm. The suggested schemes were tested in two simulation studies and were applied to a real example of measuring the critical dimensions of a 2-dimensional high-aspect-ratio structure of a wafer in semiconductor manufacturing.
Deep learning, which has recently shown excellent performance, has a problem that the amount of computation and required memory are large. Model compression is very useful because it saves memory and reduces storage size while maintaining model performance. Model compression methods reduce the number of edges by pruning weights that are deemed unnecessary in the calculation. Existing weight pruning methods using ADMM construct an optimization problem by a layer-by-layer addition of pre-defined removal-ratio constraints. Decomposing into two subproblems through the ADMM process, one can solve them through gradient descent and projection. However, the layer-by-layer removal ratios must be structurally specified, causing a sharp increase in training time due to a large number of parameters, and hardly feasible to use for large models that actually require weight pruning. Our proposed method performs weight pruning, producing similar performance, by setting a global removal ratio for the entire model without prior knowledge of structural characteristics in order to solve the shortcomings of the existing ADMM weight-pruning methods. To effectively avoid performance degradation, the method removes a relatively small number of previous layers in charge of feature extraction. Experiments show high-quality performance, not necessarily setting layer-by-layer removal ratios. Additionally, experiments increasing layers yield an insight for feature extraction in pruned layers. The experiment of the proposed method to the LeNet-5 model using MNIST data results in a higher compression ratio of 99.3% outperforming those of other existing algorithms. We also demonstrate the effectiveness of the proposed method in YOLOv4, an object detection model requiring substantial computation.